Metabolite Profiling Identifies Pathways Associated With Metabolic Risk in Humans Susan Cheng, MD; Eugene P. Rhee, MD; Martin G. Larson, ScD; Gregory D. Lewis, MD; Elizabeth L. McCabe, MS; Dongxiao Shen, PhD; Melinda J. Palma, BS; Lee D. Roberts, PhD; Andre Dejam, MD, PhD; Amanda L. Souza, MS; Amy A. Deik, BS; Martin Magnusson, MD, PhD; Caroline S. Fox, MD, MPH; Christopher J. O’Donnell, MD, MPH; Ramachandran S. Vasan, MD; Olle Melander, MD, PhD; Clary B. Clish, PhD; Robert E. Gerszten, MD*; Thomas J. Wang, MD* Background—Although metabolic risk factors are known to cluster in individuals who are prone to developing diabetes mellitus and cardiovascular disease, the underlying biological mechanisms remain poorly understood. Methods and Results—To identify pathways associated with cardiometabolic risk, we used liquid chromatography/mass spectrometry to determine the plasma concentrations of 45 distinct metabolites and to examine their relation to cardiometabolic risk in the Framingham Heart Study (FHS; n⫽1015) and the Malmo¨ Diet and Cancer Study (MDC; n⫽746). We then interrogated significant findings in experimental models of cardiovascular and metabolic disease. We observed that metabolic risk factors (obesity, insulin resistance, high blood pressure, and dyslipidemia) were associated with multiple metabolites, including branched-chain amino acids, other hydrophobic amino acids, tryptophan breakdown products, and nucleotide metabolites. We observed strong associations of insulin resistance traits with glutamine (standardized regression coefficients, ⫺0.04 to ⫺0.22 per 1-SD change in log-glutamine; P⬍0.001), glutamate (0.05 to 0.14; P⬍0.001), and the glutamine-toglutamate ratio (⫺0.05 to ⫺0.20; P⬍0.001) in the discovery sample (FHS); similar associations were observed in the replication sample (MDC). High glutamine-to-glutamate ratio was associated with lower risk of incident diabetes mellitus in FHS (odds ratio, 0.79; adjusted P⫽0.03) but not in MDC. In experimental models, administration of glutamine in mice led to both increased glucose tolerance (P⫽0.01) and decreased blood pressure (P⬍0.05). Conclusions—Biochemical profiling identified circulating metabolites not previously associated with metabolic traits. Experimentally interrogating one of these pathways demonstrated that excess glutamine relative to glutamate, resulting from exogenous administration, is associated with reduced metabolic risk in mice. (Circulation. 2012;125:2222-2231.) Key Words: epidemiology 䡲 metabolic syndrome 䡲 metabolomics 䡲 risk factors
C
the metabolic syndrome is high, affecting up to 25% of adults in the United States, and is increasing.5
ertain clinical risk factors are known to cluster in individuals who are prone to developing diabetes mellitus and future cardiovascular events.1 These risk factors are generally associated with insulin resistance and include abdominal obesity, elevated fasting glucose levels, hypertension, and dyslipidemia. The term metabolic syndrome has been used to describe a clustering of these traits among individuals.2– 4 The prevalence of
Clinical Perspective on p 2231 Pathways underlying metabolic risk factors in humans remain unknown but are likely related to derangements in primary metabolism.6 Recent advances in liquid chromatog-
Continuing medical education (CME) credit is available for this article. Go to http://cme.ahajournals.org to take the quiz. Received September 13, 2011; accepted March 12, 2012. From the Framingham Heart Study of the National Heart, Lung and Blood Institute and Boston University School of Medicine, Framingham, MA (S.C., M.G.L., E.L.M., C.S.F., C.J.O., R.S.V., T.J.W.); Cardiology Division (S.C., G.D.L., E.L.M., D.S., M.J.P.. L.D.R., A.D., C.J.O., R.E.G., T.J.W.), Cardiovascular Research Center (R.E.G., T.J.W.), Cardiovascular Research Center (E.P.R., G.D.L., R.E.G.), and Renal Division (E.P.R.), Massachusetts General Hospital, Harvard Medical School, Boston, MA; Division of Cardiovascular Medicine (S.C.) and Division of Endocrinology (C.S.F.), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; Department of Mathematics and Statistics, Boston University, Boston, MA (M.G.L.); Department of Biostatistics, Boston University School of Public Health, Boston, MA (M.G.L.); Cardiology and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA (R.S.V.); National Heart, Lung and Blood Institute, Division of Intramural Research, Bethesda, MD (C.S.F., C.J.O.); Broad Institute of MIT and Harvard University, Cambridge, MA (E.P.R., G.D.L., A.L.S., A.A.D., C.B.C., R.E.G.); and Department of Clinical Sciences, Lund University, Malmo¨, Sweden (M.M., O.M.). *Drs Gerszten and Wang contributed equally to this article. Guest Editor for this article was Susan G. Lakoski, MD. The online-only Data Supplement is available with this article at http://circ.ahajournals.org/lookup/suppl/doi:10.1161/CIRCULATIONAHA. 111.067827/-/DC1. Correspondence to Thomas J. Wang, MD, Cardiology Division, GRB-800, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114 (e-mail
[email protected]); or Robert E. Gerszten, MD, Cardiology Division and Cardiovascular Research Center, Massachusetts General Hospital East, 149 13th St, Charlestown, MA 02129 (e-mail
[email protected]). © 2012 American Heart Association, Inc. Circulation is available at http://circ.ahajournals.org
DOI: 10.1161/CIRCULATIONAHA.111.067827
2222
Cheng et al raphy tandem mass spectrometry allow the acquisition of high-throughput profiles of the metabolic status of whole organisms (eg, metabolomics), providing a comprehensive assessment of molecules that are substrates or products of metabolic pathways.7–11 Performing metabolite profiling in individuals with metabolic disorders could elucidate potential roles for specific metabolites in the development of metabolic disease and its sequelae.12–15 Therefore, we performed metabolite profiling in 2 community-based cohorts comprising individuals spanning a spectrum of metabolic risk. We then performed focused experimental studies in an animal model to test whether interventions aimed at modifying an adverse metabolite profile could attenuate manifestations of metabolic disease.
Methods Human Study Samples All human study protocols were approved by the Institutional Review boards of Boston University Medical Center, Massachusetts General Hospital, and Lund University (Sweden). All study participants provided written informed consent. The Framingham Heart Study (FHS) offspring cohort was formed in 1971 with the enrollment of 5124 individuals in a community-based longitudinal cohort study.16 Of the 2413 attendees who were free of diabetes mellitus and cardiovascular disease at the fifth examination cycle (1991–1995), 650 had metabolite profiling performed as part of 2 nested casecontrol studies designed to investigate predictors of diabetes mellitus and cardiovascular disease; an additional 365 randomly selected individuals also had metabolite profiling performed.17 Thus, 1015 individuals were eligible for the present analyses. Findings in FHS were examined for replication in the Malmo¨ Diet and Cancer Study (MDC), an investigation of 6103 individuals who were originally enrolled between 1991 and 1996 as part of a longitudinal population-based epidemiological cohort. Of the 4577 participants who had complete covariate data and were free of diabetes mellitus and cardiovascular disease at the original examination, 746 had metabolite profiling performed as part of a nested case-control study modeled after that in FHS; these individuals make up the replication sample for the present analyses.
Clinical and Dietary Assessment All FHS and MDC participants underwent a baseline examination in addition to longitudinal surveillance for incident diabetes mellitus, as described in the Methods section in the online-only Data Supplement.
Metabolite Profiling For blood specimens collected from the FHS and MDC study participants, profiles of plasma metabolites were obtained as previously described17 (see the Methods section in the online-only Data Supplement for details).
Murine Studies Weight-matched C57Bl6 mice were obtained at 7 weeks of age from The Jackson Laboratories (Bar Harbor, ME) and were housed in pairs with free access to water. Details on the experimental models of glucose tolerance and blood pressure response are provided in the Methods section in the online-only Data Supplement.
Statistical Analyses All metabolite values were natural logarithmically transformed because of their nonnormal distribution and then standardized (to
Metabolite Profiles and Metabolic Risk
2223
mean⫽0, SD⫽1) within each cohort (FHS and MDC). Age- and sex-adjusted Pearson correlation coefficients were estimated to determine correlations between metabolites known to cluster within well-defined groups (amino acids, urea cycle metabolites, etc.) in each study sample. Regression analyses were performed in each study sample to examine the relation of each metabolite (predictor variable) with each clinical metabolic trait (response variables): body mass index (BMI), waist circumference, fasting glucose, log fasting insulin, log homeostasis model assessment of insulin resistance (HOMA), systolic blood pressure (SBP), diastolic blood pressure (DBP), log triglycerides, and high-density lipoprotein (HDL) cholesterol. We analyzed each trait against each metabolite with individual regressions, adjusting for sex and age. In secondary analyses, we adjusted for BMI in addition to age and sex. Regression analyses were performed with mixed linear models18 that accounted for the sampling algorithm. Given the 45 metabolites analyzed, we used a Bonferroni-corrected P value threshold of 0.001 (0.05/45) to account for the number of metabolites analyzed. Because the majority of metabolites were correlated within well-defined biological groups (amino acids, urea cycle metabolites, etc.), this correction was conservative. We estimated statistical power to detect associations in the FHS and MDC samples. At the specified significance threshold of 0.001, we had ⬎80% power to detect association of a trait with a metabolite if the true partial correlation was at least 0.129 (FHS) or 0.151 (MDC). In tertiary analyses, we analyzed the relation of select metabolites (based on cross-sectional analyses results) with risk of future diabetes mellitus in the FHS sample of individuals who were identified as diabetes cases or randomly selected controls (n⫽601), all of whom were free of diabetes mellitus at baseline. These analyses were performed with logistic regression models adjusted for age, sex, BMI, and baseline fasting glucose. Analyses of data from the experimental models of glucose tolerance and blood pressure response are described in the Methods section in the online-only Data Supplement. All analyses were performed with SAS software version 9.1.3 (SAS Institute, Cary, NC).
Results Characteristics of the human study sample, including participants of the FHS and MDC, are shown in Table 1 and Table I in the online-only Data Supplement, respectively. In FHS, the prevalence of obesity (BMI ⱖ30 kg/m2) was 31%, and 45% of individuals met the criteria for metabolic syndrome. In the FHS sample, mean Pearson correlations within groups of related analytes were moderate to high for branched-chain amino acids (age- and sex-adjusted r⫽0.82), the larger group of hydrophobic amino acids (r⫽0.45), and urea cycle metabolites (r⫽0.46).
Analytes Associated With Metabolic Traits In regression analyses adjusted for age and sex (Figure 1 and Table 2), multiple analytes demonstrated significant associations (P⬍0.001) with ⱖ3 categories of clinical metabolic traits, including body size (BMI or waist circumference), glucose and insulin metabolism (fasting glucose, insulin, or HOMA), SBP or DBP, and lipid abnormalities (HDL cholesterol or triglycerides). Individuals with metabolic traits had highly significant elevations in the branched-chain amino acids (leucine, isoleucine, and valine) and other hydrophobic amino acids, including alanine and the aromatic amino acids (phenylalanine and tyrosine). We identified additional metab-
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Table 1. Characteristics of the Framingham Heart Study Participants (nⴝ1015) Age, y Women, %
56⫾9 47
Body mass index, kg/m2
28.3⫾5.0
Waist circumference, cm
95.6⫾13.8
Systolic blood pressure, mm Hg
129⫾18
Diastolic blood pressure, mm Hg
76⫾10
Hypertension, % Serum triglycerides, mg/dL
41 156⫾105
Serum HDL, mg/dL
48⫾14
Fasting glucose, mg/dL
98⫾10
Fasting insulin, uU/mL
10⫾8
Metabolic syndrome, % HOMA Insulin resistance, %*
45 2.5⫾2.2 33
HDL indicates high-density lipoprotein; HOMA, homeostasis model assessment of insulin resistance. Values are shown as mean⫾SD when appropriate. *Defined as having a HOMA value ⬎75th percentile of the derivation sample.
olites not previously associated with an adverse metabolic profile, including tryptophan breakdown products (kynurenic acid, anthranilic acid) and nucleotide metabolites (xanthosine, n-carbamoyl--alanine). Notably, the majority of these analytes were not correlated with fasting glucose. After adjustment for BMI in addition to age and sex, several metabolites remained significantly associated with multiple metabolic traits, including blood pressure. These metabolites included glutamine, glutamate, proline (a derivative of glutamate), and several hydrophobic amino acids (alanine, tyrosine). The hydrophobic amino acids have been linked to insulin resistance in prior studies, as has the metabolite glutamate.19 In contrast, the association of glutamine with multiple metabolic risk factors in humans has not been reported previously. After further adjustment for BMI in addition to age and sex, glutamine was inversely associated with insulin, HOMA, SBP, DBP, and log triglycerides and positively associated with HDL (P⬍0.0001 to P⫽0.008; Table 3). Conversely, glutamate was positively associated with insulin, HOMA, SBP, DBP, and log triglycerides and inversely associated with HDL (P⬍0.0001 to P⫽0.024). Because glutamine and glutamate are in a precursor-product relationship, we also examined the glutamine-to-glutamate ratio (Figures I–III in the online-only Data Supplement). In age- and sex-adjusted analyses, the glutamine-to-glutamate ratio was inversely associated with BMI, insulin, HOMA, SBP, DBP, and log triglycerides and positively associated with HDL (P⬍0.0001 to P⫽0.024; Table 3). In analyses of effect modification by sex on associations with lipid traits, there was no significant sex interaction on the relation of log triglycerides with glutamine, glutamate, or their ratio (P⬎0.37) in the FHS or MDC cohort. There was also no significant sex interaction on the relation of HDL with glutamine (P⬎0.15), although borderline-significant sex interactions were noted for the association of HDL with
glutamate (P⫽0.04) and the ratio (P⫽0.048). In sex-specific analyses, the directionality of relations between these metabolites and HDL was similar but attenuated in men compared with women (Tables II and III in the online-only Data Supplement). In analyses that included additional adjustment for dietary glutamine intake and physical activity, results remained unchanged (data not shown). We repeated the analyses of glutamate and glutamine in the MDC cohort (Table 4 and the Results section, Table III, and Figure IV in the online-only Data Supplement). In age- and sex-adjusted regression analyses (Table 4), glutamate was significantly associated with BMI, waist circumference, fasting glucose, insulin, HOMA, and log triglycerides and inversely associated with HDL (P⬍0.0001 to P⫽0.002). Glutamine was inversely associated with BMI, waist circumference, fasting glucose, insulin, and HOMA (P⫽0.001 to P⫽0.02). The glutamine-to-glutamate ratio was inversely associated with BMI, waist circumference, fasting glucose, insulin, HOMA, and log triglycerides and positively associated with HDL (P⬍0.0001 to P⫽0.0002). These associations remained significant in analyses that also adjusted for BMI (Table 4 and Table III in the online-only Data Supplement).
Relation of Glutamine and Precursor Amino Acids to Incident Diabetes Mellitus in Humans In a sample of 601 participants from the FHS cohort who were free of diabetes mellitus at baseline, we examined the relation of glutamine, glutamate, and the glutamine-toglutamate ratio with incident diabetes mellitus over 12 years. Multivariable logistic regression analyses revealed that glutamine (adjusted odds ratio, 0.83; 95% confidence interval, 0.68 –1.02; P⫽0.08), glutamate (odds ratio, 1.29; 95% confidence interval, 1.04 –1.60; P⫽0.02), and the glutamine-toglutamate ratio (odds ratio, 0.79; 95% confidence interval, 0.64 – 0.98; P⫽0.03) were associated with the risk of diabetes mellitus after adjustment for age, sex, BMI, and baseline fasting glucose. The glutamine-to-glutamate ratio was only weakly correlated with the branched-chain amino acids, which we have previously identified as predictors of incident diabetes mellitus (r⫽⫺0.25 with isoleucine, r⫽⫺0.15 with leucine, and r⫽⫺0.07 with valine).17 Accordingly, the glutamine-to-glutamate ratio was associated with decreased risk for future diabetes mellitus even after adjustment for the branched-chain amino acids (data not shown). In a sample of 409 participants from the MDC cohort free of diabetes mellitus at baseline, glutamine was inversely associated with incident diabetes mellitus as captured in a registry database (odds ratio, 0.81; 95% confidence interval, 0.67– 0.99; P⫽0.039); however, relations of glutamate (P⫽0.31) and the glutamine-to-glutamate ratio (P⫽0.91) were nonsignificant.
Amino Acid Administration and Metabolic Phenotypes in Mice To test the hypothesis that glutamine or glutamate administration modulates glucose tolerance, we performed a dietary intervention study in mice. Three groups of C57Bl6 mice received 1 of 3 diets for 8 weeks: glutamine plus standard
Cheng et al
Metabolite Profiles and Metabolic Risk
2225
Figure 1.  Coefficients and P values generated from age- and sex-adjusted regression analyses of the relation of each metabolite (standardized and log transformed) with insulin resistance phenotypes and metabolic traits (standardized) in the Framingham Heart Study sample. BMI indicates body mass index; WC, waist circumference; HOMA, homeostasis model assessment; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; HDL, high-density lipoprotein; 5-HIAA, 5-Hydroxyindoleacetic acid; GABA, gamma-aminobutyric acid; and NMMA, N-monomethyl-L-arginine.
chow (n⫽10), glutamate plus standard chow (n⫽10), and standard chow alone (n⫽10). Baseline weight and fasting glucose did not differ significantly among the 3 groups. There was no significant difference in the change in weight (P⫽0.19) or total caloric intake (P⫽0.25) across the 3 groups. At the end of the dietary intervention and after a 6-hour fast, all animals received a glucose load injected into the peritoneal cavity (intraperitoneal glucose tolerance test). Compared with mice in the control and glutamate groups, mice in the glutamine group had the lowest plasma glucose levels measured at each time point after glucose administration. Total glucose excursions, represented by the area under the curve of serial glucose measures, are shown in Figure 2A and were significantly lower in the glutamine group compared with the other groups (P⫽0.01). At the end of the dietary intervention, metabolite profiling confirmed that circulating levels of glutamine and glutamate
were significantly higher in the glutamine- and glutamate-fed mice, respectively, compared with the control group. Metabolite profiling also demonstrated that several analytes had a strong inverse correlation with directly measured levels of glutamine, including the branched-chain amino acids and other hydrophobic amino acids: isoleucine (r⫽⫺0.65, P⫽0.002), leucine (r⫽⫺0.73, P⬍0.001), valine (r⫽⫺0.62, P⫽0.003), tryptophan (r⫽⫺0.62, P⫽0.003), and tyrosine (r⫽⫺0.68, P⬍0.001). On the other hand, there was a strong positive correlation between glutamine and citrulline: r⫽0.77 (P⬍0.0001). There were significantly higher plasma concentrations of both glutamine (P⬍0.01) and citrulline (P⫽0.01) in the glutamine-fed mice compared with the control group (Figure 2B). To test the hypothesis that glutamine administration modulates blood pressure, we performed intraperitoneal injection of either glutamine plus saline or saline alone (vehicle control) in 2 groups of 10 mice. Serial blood pressure
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Table 2.
Associations of Metabolites With Insulin Resistance Phenotypes and Metabolic Traits in the Framingham Heart Study Regression Coefficient (SE)*
5-HIAA Alanine
BMI
WC
⫺0.01 (0.04)
⫺0.03 (0.03)
0.16 (0.03)㛳
0.15 (0.03)㛳
Glucose
Insulin
HOMA
SBP
DBP
0.02 (0.03)
0.00 (0.04)
0.01 (0.04)
0.09 (0.03)†
0.07 (0.04)†
0.07 (0.03)‡
0.25 (0.03)㛳
0.25 (0.03)㛳
0.11 (0.03)§
0.14 (0.03)㛳
Log TG ⫺0.02 (0.04) 0.33 (0.03)㛳
HDL 0.02 (0.03) ⫺0.17 (0.03)㛳
0.03 (0.03)
0.00 (0.03)
⫺0.07 (0.03)†
0.02 (0.03)
0.01 (0.03)
0.01 (0.03)
0.01 (0.03)
0.02 (0.03)
⫺0.06 (0.03)†
a-Glycerophosphocholine
⫺0.04 (0.03)
⫺0.03 (0.03)
⫺0.08 (0.02)§
0.05 (0.03)
0.03 (0.03)
0.00 (0.03)
0.05 (0.03)
0.14 (0.03)㛳
⫺0.02 (0.03)
aminoisobutyric acid
⫺0.01 (0.03)
0.00 (0.03)
⫺0.03 (0.03)
⫺0.06 (0.03)
⫺0.06 (0.03)
⫺0.04 (0.03)
⫺0.03 (0.03)
⫺0.10 (0.03)‡
0.02 (0.03)
0.06 (0.03)
0.04 (0.03)
⫺0.05 (0.02)†
0.11 (0.03)§
⫺0.06 (0.03)
⫺0.01 (0.03)
0.07 (0.03)†
⫺0.03 (0.03)
Allantoin
Anthranilic acid Arginine
0.08 (0.03)‡
0.07 (0.03)†
0.06 (0.03)†
0.10 (0.03)‡
0.03 (0.03)
0.02 (0.03)
0.05 (0.03)
0.05 (0.03)
0.04 (0.03)
0.01 (0.03)
Asparagine
⫺0.10 (0.03)§
⫺0.10 (0.03)§
0.02 (0.02)
⫺0.22 (0.03)㛳
⫺0.20 (0.03)㛳
⫺0.07 (0.03)†
⫺0.08 (0.03)†
⫺0.19 (0.03)㛳
Aspartate
⫺0.03 (0.03)
⫺0.04 (0.03)
⫺0.07 (0.03)†
⫺0.04 (0.03)
⫺0.05 (0.03)
0.03 (0.03)
0.02 (0.03)
0.02 (0.03)
Betaine
⫺0.04 (0.03)
⫺0.05 (0.03)
Carnitine
0.15 (0.03)㛳
0.03 (0.03)
⫺0.14 (0.03)㛳
⫺0.14 (0.03)㛳
⫺0.05 (0.03)
⫺0.04 (0.03)
⫺0.19 (0.03)㛳
0.16 (0.03)㛳
0.05 (0.03)
0.10 (0.03)‡
0.11 (0.03)§
⫺0.01 (0.03)
⫺0.02 (0.03)
0.11 (0.03)§
⫺0.01 (0.03)
0.05 (0.03)
0.04 (0.03)
0.05 (0.03)
0.06 (0.03)†
0.01 (0.02)
0.04 (0.03)
0.04 (0.03)
0.03 (0.03)
0.04 (0.03)
⫺0.02 (0.03) ⫺0.04 (0.03)
⫺0.03 (0.03) ⫺0.15 (0.03)㛳 ⫺0.05 (0.03)
Choline
0.10 (0.03)§
0.09 (0.03)‡
Cis/trans hydroxyproline
0.02 (0.03)
0.01 (0.03)
Citrulline
⫺0.07 (0.03)†
⫺0.07 (0.03)†
0.01 (0.03)
⫺0.04 (0.03)
⫺0.04 (0.03)
⫺0.04 (0.03)
⫺0.01 (0.03)
Cotinine
⫺0.07 (0.03)†
0.00 (0.03)
0.00 (0.02)
⫺0.02 (0.03)
⫺0.02 (0.03)
⫺0.04 (0.03)
⫺0.08 (0.03)‡
⫺0.02 (0.03)
⫺0.03 (0.03)
0.05 (0.04)
⫺0.03 (0.03)
0.01 (0.03)
0.03 (0.03)
⫺0.04 (0.04)
⫺0.05 (0.03)
Creatine Creatinine
0.11 (0.03)‡ ⫺0.01 (0.03)
0.14 (0.03)㛳 ⫺0.01 (0.03)
0.01 (0.03) ⫺0.02 (0.03)
0.09 (0.03)‡ 0.01 (0.03)
0.05 (0.03)
⫺0.01 (0.03) ⫺0.01 (0.03) ⫺0.09 (0.03)‡
0.01 (0.03)
⫺0.02 (0.03)
0.02 (0.03)
⫺0.17 (0.03)㛳
0.06 (0.03)†
⫺0.02 (0.03)
⫺0.01 (0.03)
0.03 (0.03)
0.06 (0.03)
0.04 (0.03)
⫺0.04 (0.03)
⫺0.07 (0.03)†
0.00 (0.03)
⫺0.09 (0.03)‡
⫺0.09 (0.03)‡
⫺0.09 (0.03)‡
⫺0.07 (0.03)†
⫺0.12 (0.03)§
⫺0.01 (0.03)
0.00 (0.03)
0.03 (0.03)
0.02 (0.03)
0.02 (0.03)
0.02 (0.03)
0.07 (0.03)†
0.08 (0.03)‡
⫺0.04 (0.03)
Dimethylglycine
0.10 (0.03)‡
0.11 (0.03)§
GABA
0.02 (0.03)
0.03 (0.03)
Glycine
⫺0.09 (0.03)‡
Histidine
0.02 (0.03)
Isoleucine
0.22 (0.03)㛳
0.21 (0.03)㛳
Kynurenic acid
0.13 (0.03)㛳
0.14 (0.03)㛳
Leucine
0.19 (0.03)㛳
0.19 (0.03)㛳
Lysine
0.07 (0.03)†
0.06 (0.03)
⫺0.03 (0.03)
0.10 (0.03)‡ 0.02 (0.03)
0.13 (0.03)㛳
0.08 (0.03)‡ ⫺0.04 (0.03)
⫺0.01 (0.03) 0.05 (0.03)† ⫺0.02 (0.03) 0.00 (0.03)
0.11 (0.03)§
0.10 (0.03)‡
0.26 (0.03)㛳
0.25 (0.03)㛳
0.09 (0.03)‡
0.11 (0.03)‡
0.29 (0.03)㛳
⫺0.28 (0.03)㛳
0.08 (0.03)‡
0.09 (0.03)‡
0.09 (0.03)‡
0.14 (0.03)㛳
0.04 (0.03)
⫺0.04 (0.03)
0.23 (0.03)㛳
0.22 (0.03)㛳
0.04 (0.03)
0.08 (0.03)†
0.27 (0.03)㛳
⫺0.25 (0.03)㛳
0.06 (0.03)
0.06 (0.03)
0.02 (0.03)
0.04 (0.03)
0.14 (0.03)㛳
⫺0.07 (0.03)† ⫺0.03 (0.03)
Methionine
0.05 (0.03)
0.07 (0.03)†
⫺0.03 (0.03)
0.08 (0.03)‡
0.08 (0.03)†
0.01 (0.03)
0.03 (0.03)
0.04 (0.03)
n-Carbamoyl--alanine
0.15 (0.03)㛳
0.12 (0.03)㛳
0.02 (0.03)
0.19 (0.03)㛳
0.19 (0.03)㛳
0.08 (0.03)‡
0.11 (0.03)§
0.13 (0.03)㛳
⫺0.08 (0.03)‡
Niacinamide
0.00 (0.03)
0.01 (0.03)
⫺0.01 (0.03)
0.09 (0.03)‡
0.09 (0.03)‡
0.02 (0.03)
0.01 (0.03)
0.18 (0.03)㛳
⫺0.08 (0.03)‡
⫺0.01 (0.03)
⫺0.01 (0.03)
⫺0.07 (0.03)†
0.00 (0.03)
0.06 (0.03)
0.07 (0.03)†
NMMA
0.02 (0.03)
0.02 (0.03)
0.03 (0.03)
Ornithine
0.10 (0.03)§
0.10 (0.03)§
0.00 (0.03)
⫺0.02 (0.03) 0.10 (0.03)‡
⫺0.02 (0.03) 0.10 (0.03)‡
⫺0.05 (0.03) ⫺0.16 (0.03)㛳
Phenylalanine
0.23 (0.03)㛳
0.21 (0.03)㛳
0.00 (0.03)
0.21 (0.03)㛳
0.21 (0.03)㛳
0.09 (0.03)‡
0.09 (0.03)‡
0.08 (0.03)†
⫺0.13 (0.03)㛳
Proline
0.12 (0.03)㛳
0.11 (0.03)§
0.06 (0.03)†
0.19 (0.03)㛳
0.19 (0.03)㛳
0.10 (0.03)§
0.12 (0.03)㛳
0.17 (0.03)㛳
⫺0.17 (0.03)㛳
0.02 (0.03)
0.01 (0.03)
0.02 (0.03)
Serotonin
Serine
⫺0.04 (0.03)
⫺0.03 (0.03)
⫺0.01 (0.02)
Taurine
⫺0.06 (0.03)†
⫺0.06 (0.03)
⫺0.03 (0.03)
⫺0.04 (0.03)
⫺0.17 (0.03)㛳
0.02 (0.03)
⫺0.02 (0.03)
⫺0.02 (0.03)
⫺0.04 (0.03)
⫺0.03 (0.03)
0.03 (0.03)
0.00 (0.03)
⫺0.02 (0.03)
⫺0.03 (0.03)
⫺0.04 (0.03)
⫺0.02 (0.03)
⫺0.02 (0.03)
⫺0.04 (0.03)
⫺0.04 (0.03)
⫺0.02 (0.03)
⫺0.02 (0.03)
Thiamine
0.02 (0.03)
0.05 (0.03)
0.02 (0.02)
⫺0.01 (0.03)
0.00 (0.03)
0.00 (0.03)
0.00 (0.03)
0.01 (0.03)
0.02 (0.03)
Threonine
⫺0.01 (0.03)
0.01 (0.03)
0.01 (0.02)
⫺0.01 (0.03)
0.00 (0.03)
0.02 (0.03)
0.03 (0.03)
⫺0.08 (0.03)‡
Thyroxine
0.01 (0.03)
0.00 (0.03)
⫺0.05 (0.03)
0.02 (0.03)
0.01 (0.03)
0.03 (0.03)
⫺0.01 (0.03)
0.00 (0.03)
Trimethylamine-N-oxide
0.00 (0.03)
0.00 (0.03)
0.01 (0.03)
0.00 (0.03)
0.00 (0.03)
⫺0.01 (0.03)
0.00 (0.03)
⫺0.04 (0.03)
Tryptophan
0.04 (0.03)
0.03 (0.03)
⫺0.06 (0.03)†
0.09 (0.03)‡
0.08 (0.03)†
0.01 (0.03)
0.05 (0.03)
0.04 (0.03)
0.02 (0.03) ⫺0.01 (0.03) ⫺0.01 (0.03) ⫺0.10 (0.03)‡
Tyrosine
0.26 (0.03)㛳
0.24 (0.03)㛳
0.00 (0.03)
0.25 (0.03)㛳
0.24 (0.03)㛳
0.14 (0.03)†
0.14 (0.03)㛳
0.18 (0.03)㛳
⫺0.11 (0.03)§
Valine
0.21 (0.03)㛳
0.20 (0.03)㛳
0.02 (0.03)
0.24 (0.03)㛳
0.23 (0.03)㛳
0.05 (0.03)
0.07 (0.03)†
0.23 (0.03)㛳
⫺0.24 (0.03)㛳
0.12 (0.03)§
⫺0.15 (0.03)㛳
0.10 (0.03)‡
0.07 (0.03)†
0.02 (0.03)
0.10 (0.03)§
0.10 (0.03)§
0.07 (0.03)†
Glutamine/isoleucine ratio
Xanthosine
⫺0.10 (0.03)‡
⫺0.09 (0.03)‡
0.01 (0.02)
⫺0.22 (0.03)㛳
⫺0.21 (0.03)㛳
⫺0.12 (0.03)†
⫺0.11 (0.03)§
0.06 (0.03)
⫺0.27 (0.03)㛳
0.14 (0.03)㛳
Glutamine/leucine ratio
⫺0.09 (0.03)‡
⫺0.08 (0.03)‡
0.01 (0.02)
⫺0.21 (0.03)㛳
⫺0.20 (0.03)㛳
⫺0.11 (0.03)§
⫺0.10 (0.03)‡
⫺0.27 (0.03)㛳
0.13 (0.03)㛳
Glutamine/valine ratio
⫺0.09 (0.03)‡
⫺0.08 (0.03)‡
0.00 (0.02)
⫺0.21 (0.03)㛳
⫺0.21 (0.03)㛳
⫺0.11 (0.03)†
⫺0.10 (0.03)‡
⫺0.26 (0.03)㛳
0.12 (0.03)㛳
BMI indicates body mass index; WC, waist circumference; HOMA, homeostasis model assessment of insulin resistance; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; HDL, high-density lipoprotein cholesterol; 5-HIAA, 5-Hydroxyindoleacetic acid; GABA, gamma-aminobutyric acid; and NMMA, N-monomethyl-L-arginine. *Regression coefficients represent the standardized change in dependent variable per 1-SD change in the log-transformed metabolite. For example, a 1-SD increment in log alanine was associated with a 0.25-SD change in insulin or 2 U/mL. Separate regressions were fitted for each combination of trait (response) and metabolite (predictor), adjusted for age and sex. †P⬍0.05. ‡P⬍0.01. §P⬍0.001. 㛳P⬍0.0001.
Cheng et al Table 3.
Metabolite Profiles and Metabolic Risk
2227
Associations of Select Metabolites With Insulin Resistance Phenotypes and Metabolic Traits in the Framingham Heart Study Regression Coefficient (P)* BMI
WC
Glucose
Insulin
HOMA
SBP
DBP
Log TG
HDL
Models adjusting for age and sex Glutamate
0.08 (0.011)
⫺0.09 (0.001)
0.05 (0.07)
0.00 (0.90)
0.11 (0.0004)
0.10 (0.0006)
0.08 (0.003)
0.09 (0.005)
0.14 (⬍0.0001)
Glutamine
⫺0.05 (0.09)
⫺0.04 (0.16)
0.01 (0.78)
⫺0.17 (⬍0.0001)
⫺0.16 (⬍0.0001)
⫺0.10 (0.0003)
⫺0.08 (0.005)
⫺0.22 (⬍0.0001)
0.08 (0.008)
Glutamine/glutamate ratio
⫺0.07 (0.024)
⫺0.05 (0.08)
0.00 (0.91)
⫺0.15 (⬍0.0001)
⫺0.15 (⬍0.0001)
⫺0.10 (0.0003)
⫺0.09 (0.002)
⫺0.20 (⬍0.0001)
0.09 (0.002)
⫺0.08 (0.008)
Models adjusting for age, sex, and BMI Glutamate
…
⫺0.01 (0.54)
⫺0.01 (0.84)
0.07 (0.009)
0.07 (0.011)
0.07 (0.016)
0.07 (0.024)
0.13 (⬍0.0001)
Glutamine
…
0.00 (0.86)
0.01 (0.62)
⫺0.14 (⬍0.0001)
⫺0.13 (⬍0.0001)
⫺0.09 (0.0012)
⫺0.07 (0.017)
⫺0.20 (⬍0.0001)
0.06 (0.023)
Glutamine/glutamate ratio
…
0.01 (0.69)
0.01 (0.69)
⫺0.12 (⬍0.0001)
⫺0.11 (⬍0.0001)
⫺0.09 (0.0017)
⫺0.08 (0.011)
⫺0.19 (⬍0.0001)
0.08 (0.007)
BMI indicates body mass index; WC, waist circumference; HOMA, homeostasis model assessment of insulin resistance; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; and HDL, high-density lipoprotein cholesterol. *Regression coefficients represent the standardized change in dependent variable per 1-SD change in the log-transformed metabolite. For example, a 1-SD increment in log glutamine was associated with a ⫺0.17-SD change in insulin or ⫺1.4 U/mL. Separate regressions were fitted for each combination of trait (response) and metabolite (predictor).
the metabolic syndrome. Therefore, we applied metabolite profiling in 2 community-based cohorts and observed that individuals with metabolic risk factors have higher circulating concentrations of glutamate, branched-chain amino acids, and other amino acid derivatives and lower concentrations of glutamine. We observed this metabolomic signature in association with multiple components of the metabolic syndrome, including central adiposity, glucose intolerance, dyslipidemia, and hypertension. Furthermore, we found that an excess of glutamine relative to glutamate in the circulation was associated with a reduced risk of future diabetes mellitus. Concordant with the findings in humans, we observed that glutamine supplementation improved glucose tolerance and lowered blood pressure in mice. Together, these data suggest that diminished glutamine, particularly in relation to its precursor amino acids, not only is a marker of metabolic risk but also may contribute to the development of metabolic disease. Our study also identified unanticipated associations
measurements were recorded with the CODA noninvasive tail blood pressure system (Kent Scientific, Torrington, CT). SBP (P⫽0.016), DBP (P⫽0.037), and mean arterial pressure (P⫽0.027) were significantly reduced in glutamine-treated mice versus controls (Figure 2C). As in the feeding experiment, levels of both glutamine (P⬍0.001) and citrulline (P⫽0.04), a known precursor to nitric oxide, were significantly higher in the glutamine-treated animals (Figure 2D). In a parallel experiment of glutamate administration, no significant difference in blood pressure was observed between the mice given glutamate plus saline versus those given saline alone (data not shown).
Discussion High-throughput metabolite profiling provides the opportunity to perform a systematic, unbiased investigation of the possible pathways underlying complex phenotypes such as
Table 4. Associations of Select Metabolites With Insulin Resistance Phenotypes and Metabolic Traits in the Malmo¨ Diet and Cancer Study Regression Coefficient (P)* BMI
WC
Glucose
Insulin
HOMA
SBP
DBP
Log TG
HDL
Models adjusting for age and sex Glutamate
0.16 (⬍0.0001)
0.17 (⬍0.0001)
0.09 (0.002)
0.14 (0.0001)
0.17 (⬍0.0001)
0.08 (0.031)
0.05 (0.15)
0.23 (⬍0.0001)
⫺0.15 (⬍0.0001)
Glutamine
⫺0.12 (0.001)
⫺0.08 (0.006)
⫺0.07 (0.022)
⫺0.10 (0.008)
⫺0.11 (0.006)
⫺0.05 (0.15)
⫺0.03 (0.44)
⫺0.02 (0.52)
0.06 (0.081)
Glutamine/glutamate ratio
⫺0.19 (⬍0.0001)
⫺0.19 (⬍0.0001)
⫺0.11 (0.0002)
⫺0.17 (⬍0.0001)
⫺0.20 (⬍0.0001)
⫺0.09 (0.010)
⫺0.06 (0.10)
⫺0.22 (⬍0.0001)
0.17 (⬍0.0001)
Models adjusting for age, sex, and BMI Glutamate
…
0.06 (⬍0.0001)
0.09 (0.004)
0.10 (0.006)
0.05 (0.19)
0.02 (0.61)
0.19 (⬍0.0001)
Glutamine
…
0.00 (0.74)
⫺0.07 (0.038)
⫺0.07 (0.069)
⫺0.07 (0.063)
0.12 (0.0006)
⫺0.03 (0.45)
0.00 (0.97)
0.01 (0.76)
Glutamine/glutamate ratio
…
⫺0.05 (0.0002)
⫺0.11 (0.0005)
⫺0.12 (0.0009)
⫺0.14 (⬍0.0001)
⫺0.05 (0.13)
⫺0.02 (0.64)
⫺0.18 (⬍0.0001)
⫺0.11 (0.0006) 0.03 (0.41) 0.12 (0.0004)
BMI indicates body mass index; WC, waist circumference; HOMA, homeostasis model assessment of insulin resistance; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; and HDL, high-density lipoprotein cholesterol. *Regression coefficients represent the standardized change in dependent variable per 1-SD change in the log-transformed metabolite. For example, a 1-SD increment in log glutamine was associated with a ⫺0.10-SD change in insulin or ⫺1.0 U/mL. Separate regressions were fitted for each combination of trait (response) and metabolite (predictor).
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Figure 2. Experimental results. A, Mean⫾SE area under the curve (AUC) of serial glucose levels measured after intraperitoneal glucose tolerance test (IPGTT) in mice treated for 8 weeks with dietary glutamine, glutamate, or standard chow alone (control). B, Mean⫾SE measures of glutamine and citrulline are shown in mice treated with dietary glutamine, glutamate, or standard chow alone (control). C, Mean⫾SE measures of systolic (SBP), diastolic (DBP), and mean (MAP) blood pressure measurements are shown for mice after intraperitoneal injection of either glutamine plus saline (glutamine) or saline alone (control). D, Mean⫾SE measures of glutamine and citrulline are shown for mice after intraperitoneal injection of either glutamine plus saline (glutamine) or saline alone (control).
of tryptophan breakdown products (kynurenic acid, anthranilic acid) and nucleotide metabolites (xanthosine, n-carbamoyl--alanine) with cardiometabolic disease. Several patterns noted in our metabolomic data are consistent with results of prior experimental and physiological studies. Felig and colleagues19 originally reported that selected amino acids were associated with fasting insulin levels in 10 obese and 10 nonobese individuals. More recent investigations in small samples have highlighted associations of the branched-chain amino acids (isoleucine, leucine, and valine) and other hydrophobic amino acids (eg, alanine, phenylalanine, and tyrosine) with obesity, impaired glucose tolerance, and insulin resistance.12–15,19 A population study also observed an association of 24-hour urinary excretion of alanine with higher blood pressure.11 The present investigation extends the prior work by demonstrating robust and reproducible associations of circulating levels of branchedchain amino acids not only with obesity and impaired glucose tolerance but also with dyslipidemia and blood pressure in 2 large human cohorts. Our metabolomic data also highlight several patterns that have not previously been well described in association with
metabolic disease. Glutamine is best known for its role in providing intermediates to the tricarboxylic acid cycle (anapleurosis) and for its ammonia-carrying capacity, which is critical for maintaining overall nitrogen balance.20 –22 Total body glutamine is depleted in catabolic states such as trauma, critical illness, infection, and sepsis, and parenteral glutamine supplementation in the intensive care setting is associated with reduced morbidity and mortality.23 These findings have been attributed to the effects of glutamine on the regulation of white cell function, cell volume, response to cytotoxins, and cellular redox potential via glutathione-mediated pathways.21 We now demonstrate in 2 large human cohorts that glutamine is inversely associated with a wide array of metabolic traits, including hypertension and hypertriglyceridemia, in addition to measures of obesity and insulin resistance. In our data, standard deviation increments in metabolite concentrations were generally associated with meaningful differences in metabolic trait measures (eg, a 1.2- to 2.0-U/mL difference in plasma insulin). The consistent associations observed between multiple glutamine-related metabolites and metabolic traits further support the hypothesis that glutamine-cycling pathways are prominently involved in the development of metabolic risk.
Cheng et al Interestingly, a prior study of 24 adults with and without diabetes mellitus showed that glutamine supplementation was associated with improved glucose tolerance.24 In support of the hypothesis that glutamine may be more than a marker of metabolic risk, animal studies have shown that glutamine administration improves glucose and insulin metabolism in the setting of exercise25 or a high-fat diet.26 Our experimental data extend these findings by distinguishing the protective effect of glutamine versus glutamate supplementation, showing the corresponding changes in metabolite profiles, and demonstrating that the improvements in glucose tolerance are independent of changes in weight. Furthermore, we found that glutamine administration decreases blood pressure after short-term administration. Taken together, these data underscore the potentially beneficial effects of glutamine on cardiometabolic risk, which may be due to a number of mechanisms such as enhanced release of glucagon-like peptide 1, externalization of glucose transporter type 4, transcription of insulin-dependent enzymes, pancreatic -cell insulin secretion, and increased insulin sensitivity of adipose tissue.24,27,28 The association of glutamine with blood pressure may be further related to direct or indirect effects on the regulation of nitric oxide production,29 –31 as suggested by our finding of strong correlations between glutamine and citrulline, a known precursor to nitric oxide. The distinct roles of glutamine versus glutamate have not previously been highlighted. Endogenous glutamine is continually synthesized in the skeletal muscle, predominantly from glutamate but also from branched-chain amino acids. Cycling between glutamine and glutamate is regulated by the activity of glutamine synthetase and glutaminase, enzymes that have wide tissue distribution.21,32 Prior studies have observed an association of dietary glutamate with lower blood pressure33 but without accounting for its effect on endogenous glutamine levels.34 A recent investigation of metabolomic profiles in humans observed that obesity and insulin resistance are associated with an analyte identified as both glutamine and glutamate.12 Our platform distinguishes the 2 analytes, highlighting the unanticipated finding that glutamate may confer adverse metabolic risk whereas glutamine may be protective. In contrast to the likely favorable effects of glutamine, glutamate has been shown to stimulate glucagon release from pancreatic ␣ cells35 and to increase transamination of pyruvate to alanine, a strong promoter of gluconeogenesis that is abundant in obesity.12 Glutamate is also well known as a direct precursor to ␣-ketoglutarate, an intermediate in the Krebs cycle that serves as an energy source for multiple cell types and that exerts anabolic as well as anticatabolic activity.36 Several limitations of the present study merit consideration. We used a liquid chromatography tandem mass spectrometry platform that does not provide full coverage of the plasma metabolome. However, our focus on abundant metabolites permitted the measure of 45 analytes; by providing key clinical correlates for a large panel of plasma metabolites, these data complement ongoing efforts to annotate the human metabolome.37 The present analysis was focused on interrogating the relationship of glutamine (and its precursor ana-
Metabolite Profiles and Metabolic Risk
2229
lytes) with metabolic traits and potential functional roles. The biology underlying the correlates of other analytes remains the subject of future investigation. A small number of metabolites had coefficients of variation exceeding 20% but were retained in analyses for completeness; the results of analyzing these specific metabolites may have been biased to the null. As a surrogate measure of insulin resistance, HOMA-IR is less precise than measures obtained from the hyperinsulinemic euglycemic glucose clamp technique or the insulin suppression test. Nonetheless, it is regarded as a reasonably reliable surrogate in individuals without severely impaired pancreatic -cell function and is considered practical for epidemiological studies.38,39 Because the MDC cohort was relatively small and consisted exclusively of individuals previously identified as having a high baseline risk for developing incident diabetes mellitus or cardiovascular disease, analyses of incident diabetes mellitus in this cohort were limited by sampling in addition to possible ascertainment bias. Thus, additional investigations of the relation of glutamine and glutamate with incident diabetes mellitus are needed in unselected populations. Because many of the metabolites included in the present study were amino acids or amino acid precursors or derivatives, the possible associations of non–amino acid molecules with cardiometabolic risk remain a subject for further investigation. Our clinical cohorts were comprised of predominantly middle-aged to elderly individuals of European ancestry; thus, the generalizability of our findings to younger individuals and other racial/ethnic groups is not yet known. The present study demonstrates the potential value of integrating metabolomic and clinical data in human cohorts with detailed phenotyping. While highlighting select amino acids of interest, we recognize that our data relating all of the measured metabolites to metabolic traits in a large sample of ambulatory individuals may be of broader scientific interest. Indeed, we submit that several features favor the potential of our data to serve as a resource for future investigations. First, these data demonstrate the feasibility and reproducibility of analyzing the relations of metabolomic with clinical data across 2 separate community-based cohorts. Second, our results provide an important clinical dimension to numerous plasma metabolites, complementing ongoing efforts to annotate the human metabolome.37 Third, the range of observed associations between metabolites and metabolic traits highlights opportunities for further research aimed at elucidating pathways underlying cardiometabolic disease and identifying potential therapeutic targets. Because metabolites represent intermediate traits that may play functional roles (either adaptive or maladaptive) in disease pathogenesis, future applications of metabolomics technology can provide additional insights into the mechanisms by which established risk factors are associated with clinically important cardiovascular and metabolic outcomes.
Sources of Funding This work was supported by National Institutes of Health contracts N01-HC-25195 and R01-DK-HL081572, the Donald W. Reynolds Foundation, and the Leducq Foundation. Dr Cheng is supported by
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the Ellison Foundation. Dr Dejam is supported by American Heart Association grant 10CRP2660009. Dr Gerszten is supported by an American Heart Association Established Investigator Award.
Disclosures Drs Larson, Vasan, Gerszten, and Wang are named as coinventors on a patent application to the US Patent Office pertaining to metabolite predictors of diabetes mellitus. The other authors report no conflicts.
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CLINICAL PERSPECTIVE Although metabolic risk factors are known to cluster in individuals who are prone to developing diabetes mellitus and cardiovascular disease, the underlying biological mechanisms remain poorly understood. To acquire a more detailed understanding of the biochemical pathways, we applied high-throughput metabolite profiling to samples from 1761 individuals from 2 large, well-characterized clinical cohorts. We observed that the presence of metabolic risk factors (including obesity, insulin resistance, high blood pressure, and dyslipidemia) was significantly associated with variation in select metabolites, including branched-chain amino acids, other hydrophobic amino acids, tryptophan breakdown products, and nucleotide metabolites. We observed particularly strong associations of insulin resistance traits with decreased glutamine and increased glutamate. We followed up these findings in experimental models and demonstrated that glutamine administration in mice resulted in both increased glucose tolerance and decreased blood pressure. Taken together, our clinical and experimental data highlight the glutamine-glutamate metabolic pathway as a potential target for interventions aimed at attenuating metabolic risk in humans. Furthermore, by demonstrating the feasibility and utility of biochemical profiling in large clinical samples, we anticipate that our data could serve as a resource for future studies of the human metabolome and its relevance to cardiovascular and metabolic diseases. Because metabolites represent intermediate traits that may play functional roles (either adaptive or maladaptive) in disease pathogenesis, future applications of metabolomics technology can provide additional insights into the mechanisms by which established risk factors are associated with clinically important outcomes.
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